A Randomized Trial Assessing the Effectiveness of Ezetimibe in South Asian Canadians with Coronary Artery Disease or Diabetes: The INFINITY Study
Bibliographic record
Abstract
Background. There is a paucity of data regarding the effectiveness and safety of lipid-lowering treatments among South-Asian patients. Methods. Sixty-four South-Asian Canadians with coronary artery disease or diabetes and persistent hypercholesterolemia on statin therapy, were randomized to ezetimibe 10 mg/day co-administered with statin therapy (EZE + Statin) or doubling their current statin dose (STAT(2)). Primary outcome was the proportion of patients achieving target LDL-C (<2.0 mmol/L) after 6 weeks. Secondary outcomes included the change in lipid profile and the incidence of treatment-emergent adverse events through 12 weeks. Exploratory markers for vascular inflammation were assessed at baseline and 12 weeks. Results. At 6 weeks, the primary outcome was significantly higher among the EZE + Statin patients (68% versus 36%; P = 0.031) with an OR (95% CI) of 3.97 (1.19, 13.18) upon accounting for baseline LDL-C and adjusting for age. At 12 weeks, 76% of EZE + Statin patients achieved target LDL-C compared to 48% (P = 0.047) of the STAT(2) patients (adjusted OR (95% CI) = 3.31 (1.01,10.89)). No significant between-group differences in exploratory markers were observed with the exception of CRP. Conclusions. Patients receiving ezetimibe and statin were more likely to achieve target LDL-C after 6 and 12 weeks compared to patients doubling their statin dose. Ezetimibe/statin combination therapy was well tolerated among this cohort of South-Asian Canadians, without safety concerns.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".